(1) Field of Invention
The present invention relates to a cognitive-neural method for image analysis and, more specifically, to a closed-loop cognitive method and system which combines bio-inspired cognitive object detection algorithms with neural signatures of human visual processing to effectively identify items of interest in visual imagery.
(2) Description of Related Art
There are two main types of approaches for rapid search and categorization of items of interest in imagery and video. The first approach uses conventional machine vision methods or bio-inspired cognitive methods. These methods either need a predefined set of objects/items of interest or have very high false alarm rates. The second class of methods is based on neural signatures of object detection. These neural methods usually break the entire image into sub-images and process Electroencephalography (EEG) data from these images and classify them based on it. This approach also suffers from high false alarms and is usually slow because the entire image is chipped and presented to the human observer.
Some systems in the art use neural signals for classification of images into two categories: those that contain items of interest and those that do not. Thus, these systems are restricted to identifying items of interest that include single objects. In addition, the entire large image needs to be divided into small images (called chips) and presented as Rapid Serial Visual Presentation (RSVP) to an operator, which is slow. These systems also suffer from high false alarm rates. Other systems only deal with neural based classification and suffer from similar problems.
There exist in the art cognitive object detection methods that are based on bio-inspired models of human visual attention and stand out as the most established and widely referenced in this field. However, these methods return single pixels as salient points of attention and do not provide any region or extent of the object (i.e., its size and shape), as they usually spotlight an arbitrary region around a salient point. Often a single item of interest may contain multiple salient points. Thus, these methods are not very suitable or efficient for object detection in imagery/video.
Furthermore, in the area of surveillance, the ability to extract useful information from the terabytes of imagery gathered every day is limited by the number of image analysts available and the slow pace of the manual triage process. However, the ability to use this data effectively is contingent on rapid and accurate screening of surveillance imagery. Unfortunately, with the limited number of image analysts available, and the time it takes to process each image, vast numbers of images are not examined properly. The present state of image analysis lacks the speed and efficiency to adequately process the load of surveillance imagery needed in today's world.
Thus, a continuing need exists for an object detection method and system which efficiently combines the benefits of both cognitive and neural object detection methods to provide a system which can process large volumes of imagery and adapt to changes in search goals in situ.